We have presented a conversion of FrameNet to RDF, published a dataset in the LOD cloud, linked to WordNet and other lexical datasets. We have also presented a method to convert FrameNet data into knowledge patterns. For both projects, we have employed the Semion tool with SemionRules, which allows a customized and explicit transformation from RDB or XML to RDF and OWL. 
%A brief comparison of Semion to other XML to RDF conversion tools has been also included.
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The intricate semantics of FrameNet, only partly described in this paper, gets to grips with the expressive power of natural language. A fixed, ad-hoc transformation would be best for one, arbitrary for another, bad for a third.
Customization is key with lexical data because there are use cases for maintaining the semantics of the original resource, often a purely intensional one (similar to the practice of using SKOS with thesauri), as well as for morphing the original semantics to something closer to the extensional formal semantics of web ontologies. In between these two ends, there are several intermediate cases and exceptions, which make the case for tools that minimize hard-coding of the transformation semantics, and preserve the opportunity to learn and share good practices for transforming lexical resources to linked data and domain knowledge. \newline
Current ongoing work concentrates on refinement of the RDF dataset with the Berkeley FrameNet group, the generation of new links to lexical datasets as well as other relevant LOD datasets (e.g. DBpedia), the creation of the FrameNet valence dataset, which will be a substantial (about 35 million triples) resource for hybridizing semantic web and linked data, and the refinement of a recipe to produce and automatically publish FrameNet-based knowledge patterns on the ODP portal\footnote{\small{http://www.ontologydesignpatterns.org, cf.~\cite{odphandbook}}}. These knowledge patterns implement a large section of the rich knowledge pattern structure envisaged by~\cite{GangemiPresutti10}, with formal axioms, lexically motivated vocabulary, textual corpus grounding, and data grounding.
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%Finally, we discuss ongoing and future work (building a repository of transformation patterns/recipes, link existing content ODPs to FrameNet ODPs (for providing lexical grounding to them), evaluating semion, survey on methods/tools for transforming xml to rdf).
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%After that we discuss the lesson we have learnt by performing this work e.g. the need of flexible methods for transforming lexical resources to linked data, the value of having 